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Automated Decision Tree Classification of Corneal Shape

TWA, MICHAEL D. OD, MS, FAAO; PARTHASARATHY, SRINIVASAN PhD; ROBERTS, CYNTHIA PhD; MAHMOUD, ASHRAF M.; RAASCH, THOMAS W. OD, PhD, FAAO; BULLIMORE, MARK A. MCOptom, PhD, FAAO

doi: 10.1097/01.opx.0000192350.01045.6f
Articles: Original Article
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Purpose. The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods.

Methods. The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method.

Results. Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil.

Conclusion. Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems.

College of Optometry (MDT, TWR, MAB), Department of Computer Science and Engineering (SP), and Departments of Ophthalmology and Biomedical Engineering (CR, AMM), The Ohio State University, Columbus, Ohio

This study was supported by National Institutes of Health grants EY16225 and EY13359 (MDT), American Optometric Foundation Ocular Sciences Ezell Fellowship, Ameritech faculty fellowship (SP), and NIH-EY12952 (MAB).

In August 2003 and May 2005, portions of this work were presented at Mopane 2003: Astigmatism Aberrations and Vision Conference, Mopani, South Africa and ARVO, respectively.

Received May 23, 2005; accepted July 21, 2005.

© 2005 American Academy of Optometry